{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"zaksum RL.ipynb","provenance":[],"collapsed_sections":["Fiju5JVl29Iy"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"43TIvlnX4rzh","colab_type":"code","colab":{}},"source":["directory = \"drive/My Drive/Experiments/Arabic/Core/Normal/\"\n","senetences_file = \"result_core_test_100_sentence_23_12_2019_6_55pm.xml\"\n","          \n","#----------------------------------------------------#\n","result_file = directory + \"zaksum_\" + senetences_file"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jXxHXRUP6wj_","colab_type":"text"},"source":["for comparing (good resources)\n","\n","1.  [thunlp]( https://github.com/thunlp/TensorFlow-Summarization)     works with duc2003\n","2.  [textsum](https://github.com/tensorflow/models/tree/master/research/textsum )   \n","3.  [Auto Generation of Arabic News Headlines](http://cs229.stanford.edu/proj2017/final-reports/5239796.pdf)\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"XTY_XS-h4gM0","colab_type":"text"},"source":["## Helpers"]},{"cell_type":"code","metadata":{"id":"T6LHmi8k27-y","colab_type":"code","outputId":"ab924145-32ef-4f9e-95f7-63cb3e2bb396","executionInfo":{"status":"ok","timestamp":1577119428937,"user_tz":-120,"elapsed":32226,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"W_mTNHy97QZh","colab_type":"code","outputId":"9a99825a-b911-4104-c2d9-806f7eb09659","executionInfo":{"status":"ok","timestamp":1563899436288,"user_tz":-120,"elapsed":2753,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["!ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["sample_data\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"qR8WMMh14bWD","colab_type":"code","colab":{}},"source":["from __future__ import print_function\n","\n","\n","import json\n","import os\n","import pandas\n","import io\n","import sys\n","import re\n","\n","\n","class ProgressBar(object):\n","    DEFAULT = 'Progress: %(bar)s %(percent)3d%%'\n","    FULL = '%(bar)s %(current)d/%(total)d (%(percent)3d%%) %(remaining)d to go'\n","\n","    def __init__(self, total, width=40, fmt=DEFAULT, symbol='=',\n","                 output=sys.stderr):\n","        assert len(symbol) == 1\n","\n","        self.total = total\n","        self.width = width\n","        self.symbol = symbol\n","        self.output = output\n","        self.fmt = re.sub(r'(?P<name>%\\(.+?\\))d',\n","            r'\\g<name>%dd' % len(str(total)), fmt)\n","\n","        self.current = 0\n","\n","    def __call__(self):\n","        percent = self.current / float(self.total)\n","        size = int(self.width * percent)\n","        remaining = self.total - self.current\n","        bar = '[' + self.symbol * size + ' ' * (self.width - size) + ']'\n","\n","        args = {\n","            'total': self.total,\n","            'bar': bar,\n","            'current': self.current,\n","            'percent': percent * 100,\n","            'remaining': remaining\n","        }\n","        print('\\r' + self.fmt % args, file=self.output, end='')\n","\n","    def done(self):\n","        self.current = self.total\n","        self()\n","        print('', file=self.output)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZKdWqkt94kFz","colab_type":"code","colab":{}},"source":["article=[]\n","reference=[]\n","summary =[]\n","\n","import xml.etree.ElementTree\n","e = xml.etree.ElementTree.parse(directory + senetences_file).getroot()\n","\n","def clean(text):\n","  text = text.replace('_','')\n","  text = text.replace('!','')\n","  return text\n","\n","for atype in e.findall('example/article'):\n","    article.append(clean(atype.text))\n","    \n","for atype in e.findall('example/reference'):\n","     reference.append(clean(atype.text))\n","    \n","for atype in e.findall('example/summary'):\n","     summary.append(clean(atype.text))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"eHB63LH_q12Z","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":88},"outputId":"3aefe86d-4be4-4801-82a9-7e4eeed68352","executionInfo":{"status":"ok","timestamp":1577120160826,"user_tz":-120,"elapsed":741,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}}},"source":["print(article[1])\n","print(reference[1])\n","print(summary[1])"],"execution_count":24,"outputs":[{"output_type":"stream","text":["بعثه المنتخب المغربي تحل ب مالابو بلاعبين فقط انتظار المحترفين اخبارنا المغربيه الرباط وصلت البعثه الرسميه لمنتخب الوطني الاو ل اليوم الاحد اكتوبر لمطار مالابو بغينيا الاستو ائيه علي الساعه الخامسه الا ربع صباحا بالتوقيت المحلي نفس التوقيت المغربي رحله عاديه دامت ساعات انطلاقا مطار الدار البيضاء و مرورا بعاصمه الغابون ليبروفيل و تكون البعثه المغربيه حلت صباح اليوم بمالابو الاطقم التقنيه الطبيه الاداريه و الاعلاميه لمنتخب الي جانب جو اد ياميق لاعب الرجاء الرياضي و اسماعيل الحداد لاعب الوداد الرياضي علي ان يلتحق باقي الاعبين المنادي عليهم تباعا بمقر المعسكر الاعدادي لنخبه الوطنيه و سينطلق رسميا اليوم الاحد معسكر المنتخب الوطني الاو ل استعدادا لمباراه الرسميه ستجمع الاسود بمنتخب الغابون يوم السبت اكتوبر علي ارضيه ملعب فرانس فيل بمدينه فرانس فيل بالغابون و برسم الجو الاو المجموعه الثالثه الاقصائيات الافريقيه المؤهله لنهائيات كاس العالم روسيا مجموع المشاهدات مشاركه\n","بعثه المنتخب المغربي تحل ب مالابو بلاعبين فقط انتظار المحترفين\n","بعثه المنتخب المغربي التي تحل ب مالابو\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Fiju5JVl29Iy","colab_type":"text"},"source":["## Core (Bleu/rouge)"]},{"cell_type":"code","metadata":{"id":"YzO4nuAfv3se","colab_type":"code","colab":{}},"source":["#\n","\n","# -*- coding: utf-8 -*-\n","# Copyright 2017 Google Inc.\n","#\n","# Licensed under the Apache License, Version 2.0 (the \"License\");\n","# you may not use this file except in compliance with the License.\n","# You may obtain a copy of the License at\n","#\n","#      http://www.apache.org/licenses/LICENSE-2.0\n","#\n","# Unless required by applicable law or agreed to in writing, software\n","# distributed under the License is distributed on an \"AS IS\" BASIS,\n","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","# See the License for the specific language governing permissions and\n","# limitations under the License.\n","\"\"\"ROUGe metric implementation.\n","This is a modified and slightly extended verison of\n","https://github.com/miso-belica/sumy/blob/dev/sumy/evaluation/rouge.py.\n","\"\"\"\n","\n","from __future__ import absolute_import\n","from __future__ import division\n","from __future__ import print_function\n","from __future__ import unicode_literals\n","\n","import itertools\n","import numpy as np\n","\n","#pylint: disable=C0103\n","\n","\n","def _get_ngrams(n, text):\n","  \"\"\"Calcualtes n-grams.\n","  Args:\n","    n: which n-grams to calculate\n","    text: An array of tokens\n","  Returns:\n","    A set of n-grams\n","  \"\"\"\n","  ngram_set = set()\n","  text_length = len(text)\n","  max_index_ngram_start = text_length - n\n","  for i in range(max_index_ngram_start + 1):\n","    ngram_set.add(tuple(text[i:i + n]))\n","  return ngram_set\n","\n","def _split_into_words(sentences):\n","  \"\"\"Splits multiple sentences into words and flattens the result\"\"\"\n","  return list(itertools.chain(*[_.split(\" \") for _ in sentences]))\n","\n","def _get_word_ngrams(n, sentences):\n","  \"\"\"Calculates word n-grams for multiple sentences.\n","  \"\"\"\n","  assert len(sentences) > 0\n","  assert n > 0\n","\n","  words = _split_into_words(sentences)\n","  return _get_ngrams(n, words)\n","\n","def _len_lcs(x, y):\n","  \"\"\"\n","  Returns the length of the Longest Common Subsequence between sequences x\n","  and y.\n","  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n","  Args:\n","    x: sequence of words\n","    y: sequence of words\n","  Returns\n","    integer: Length of LCS between x and y\n","  \"\"\"\n","  table = _lcs(x, y)\n","  n, m = len(x), len(y)\n","  return table[n, m]\n","\n","def _lcs(x, y):\n","  \"\"\"\n","  Computes the length of the longest common subsequence (lcs) between two\n","  strings. The implementation below uses a DP programming algorithm and runs\n","  in O(nm) time where n = len(x) and m = len(y).\n","  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n","  Args:\n","    x: collection of words\n","    y: collection of words\n","  Returns:\n","    Table of dictionary of coord and len lcs\n","  \"\"\"\n","  n, m = len(x), len(y)\n","  table = dict()\n","  for i in range(n + 1):\n","    for j in range(m + 1):\n","      if i == 0 or j == 0:\n","        table[i, j] = 0\n","      elif x[i - 1] == y[j - 1]:\n","        table[i, j] = table[i - 1, j - 1] + 1\n","      else:\n","        table[i, j] = max(table[i - 1, j], table[i, j - 1])\n","  return table\n","\n","def _recon_lcs(x, y):\n","  \"\"\"\n","  Returns the Longest Subsequence between x and y.\n","  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n","  Args:\n","    x: sequence of words\n","    y: sequence of words\n","  Returns:\n","    sequence: LCS of x and y\n","  \"\"\"\n","  i, j = len(x), len(y)\n","  table = _lcs(x, y)\n","\n","  def _recon(i, j):\n","    \"\"\"private recon calculation\"\"\"\n","    if i == 0 or j == 0:\n","      return []\n","    elif x[i - 1] == y[j - 1]:\n","      return _recon(i - 1, j - 1) + [(x[i - 1], i)]\n","    elif table[i - 1, j] > table[i, j - 1]:\n","      return _recon(i - 1, j)\n","    else:\n","      return _recon(i, j - 1)\n","\n","  recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))\n","  return recon_tuple\n","\n","def rouge_n(evaluated_sentences, reference_sentences, n=2):\n","  \"\"\"\n","  Computes ROUGE-N of two text collections of sentences.\n","  Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/\n","  papers/rouge-working-note-v1.3.1.pdf\n","  Args:\n","    evaluated_sentences: The sentences that have been picked by the summarizer\n","    reference_sentences: The sentences from the referene set\n","    n: Size of ngram.  Defaults to 2.\n","  Returns:\n","    A tuple (f1, precision, recall) for ROUGE-N\n","  Raises:\n","    ValueError: raises exception if a param has len <= 0\n","  \"\"\"\n","  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:\n","    raise ValueError(\"Collections must contain at least 1 sentence.\")\n","\n","  evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences)\n","  reference_ngrams = _get_word_ngrams(n, reference_sentences)\n","  reference_count = len(reference_ngrams)\n","  evaluated_count = len(evaluated_ngrams)\n","\n","  # Gets the overlapping ngrams between evaluated and reference\n","  overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)\n","  overlapping_count = len(overlapping_ngrams)\n","\n","  # Handle edge case. This isn't mathematically correct, but it's good enough\n","  if evaluated_count == 0:\n","    precision = 0.0\n","  else:\n","    precision = overlapping_count / evaluated_count\n","\n","  if reference_count == 0:\n","    recall = 0.0\n","  else:\n","    recall = overlapping_count / reference_count\n","\n","  f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))\n","\n","  # return overlapping_count / reference_count\n","  return f1_score, precision, recall\n","\n","def _f_p_r_lcs(llcs, m, n):\n","  \"\"\"\n","  Computes the LCS-based F-measure score\n","  Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n","  rouge-working-note-v1.3.1.pdf\n","  Args:\n","    llcs: Length of LCS\n","    m: number of words in reference summary\n","    n: number of words in candidate summary\n","  Returns:\n","    Float. LCS-based F-measure score\n","  \"\"\"\n","  r_lcs = llcs / m\n","  p_lcs = llcs / n\n","  beta = p_lcs / (r_lcs + 1e-12)\n","  num = (1 + (beta**2)) * r_lcs * p_lcs\n","  denom = r_lcs + ((beta**2) * p_lcs)\n","  f_lcs = num / (denom + 1e-12)\n","  return f_lcs, p_lcs, r_lcs\n","\n","\n","def rouge_l_sentence_level(evaluated_sentences, reference_sentences):\n","  \"\"\"\n","  Computes ROUGE-L (sentence level) of two text collections of sentences.\n","  http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n","  rouge-working-note-v1.3.1.pdf\n","  Calculated according to:\n","  R_lcs = LCS(X,Y)/m\n","  P_lcs = LCS(X,Y)/n\n","  F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)\n","  where:\n","  X = reference summary\n","  Y = Candidate summary\n","  m = length of reference summary\n","  n = length of candidate summary\n","  Args:\n","    evaluated_sentences: The sentences that have been picked by the summarizer\n","    reference_sentences: The sentences from the referene set\n","  Returns:\n","    A float: F_lcs\n","  Raises:\n","    ValueError: raises exception if a param has len <= 0\n","  \"\"\"\n","  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:\n","    raise ValueError(\"Collections must contain at least 1 sentence.\")\n","  reference_words = _split_into_words(reference_sentences)\n","  evaluated_words = _split_into_words(evaluated_sentences)\n","  m = len(reference_words)\n","  n = len(evaluated_words)\n","  lcs = _len_lcs(evaluated_words, reference_words)\n","  return _f_p_r_lcs(lcs, m, n)\n","\n","\n","def _union_lcs(evaluated_sentences, reference_sentence):\n","  \"\"\"\n","  Returns LCS_u(r_i, C) which is the LCS score of the union longest common\n","  subsequence between reference sentence ri and candidate summary C. For example\n","  if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8 and\n","  c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1 is\n","  “w1 w2” and the longest common subsequence of r_i and c2 is “w1 w3 w5”. The\n","  union longest common subsequence of r_i, c1, and c2 is “w1 w2 w3 w5” and\n","  LCS_u(r_i, C) = 4/5.\n","  Args:\n","    evaluated_sentences: The sentences that have been picked by the summarizer\n","    reference_sentence: One of the sentences in the reference summaries\n","  Returns:\n","    float: LCS_u(r_i, C)\n","  ValueError:\n","    Raises exception if a param has len <= 0\n","  \"\"\"\n","  if len(evaluated_sentences) <= 0:\n","    raise ValueError(\"Collections must contain at least 1 sentence.\")\n","\n","  lcs_union = set()\n","  reference_words = _split_into_words([reference_sentence])\n","  combined_lcs_length = 0\n","  for eval_s in evaluated_sentences:\n","    evaluated_words = _split_into_words([eval_s])\n","    lcs = set(_recon_lcs(reference_words, evaluated_words))\n","    combined_lcs_length += len(lcs)\n","    lcs_union = lcs_union.union(lcs)\n","\n","  union_lcs_count = len(lcs_union)\n","  union_lcs_value = union_lcs_count / combined_lcs_length\n","  return union_lcs_value\n","\n","\n","def rouge_l_summary_level(evaluated_sentences, reference_sentences):\n","  \"\"\"\n","  Computes ROUGE-L (summary level) of two text collections of sentences.\n","  http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n","  rouge-working-note-v1.3.1.pdf\n","  Calculated according to:\n","  R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m\n","  P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n\n","  F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)\n","  where:\n","  SUM(i,u) = SUM from i through u\n","  u = number of sentences in reference summary\n","  C = Candidate summary made up of v sentences\n","  m = number of words in reference summary\n","  n = number of words in candidate summary\n","  Args:\n","    evaluated_sentences: The sentences that have been picked by the summarizer\n","    reference_sentence: One of the sentences in the reference summaries\n","  Returns:\n","    A float: F_lcs\n","  Raises:\n","    ValueError: raises exception if a param has len <= 0\n","  \"\"\"\n","  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:\n","    raise ValueError(\"Collections must contain at least 1 sentence.\")\n","\n","  # total number of words in reference sentences\n","  m = len(_split_into_words(reference_sentences))\n","\n","  # total number of words in evaluated sentences\n","  n = len(_split_into_words(evaluated_sentences))\n","\n","  union_lcs_sum_across_all_references = 0\n","  for ref_s in reference_sentences:\n","    union_lcs_sum_across_all_references += _union_lcs(evaluated_sentences,\n","                                                      ref_s)\n","  return _f_p_r_lcs(union_lcs_sum_across_all_references, m, n)\n","\n","\n","def rouge(hypotheses, references):\n","  \"\"\"Calculates average rouge scores for a list of hypotheses and\n","  references\"\"\"\n","\n","  # Filter out hyps that are of 0 length\n","  # hyps_and_refs = zip(hypotheses, references)\n","  # hyps_and_refs = [_ for _ in hyps_and_refs if len(_[0]) > 0]\n","  # hypotheses, references = zip(*hyps_and_refs)\n","\n","  # Calculate ROUGE-1 F1, precision, recall scores\n","  rouge_1 = [\n","      rouge_n([hyp], [ref], 1) for hyp, ref in zip(hypotheses, references)\n","  ]\n","  rouge_1_f, rouge_1_p, rouge_1_r = map(np.mean, zip(*rouge_1))\n","\n","  # Calculate ROUGE-2 F1, precision, recall scores\n","  rouge_2 = [\n","      rouge_n([hyp], [ref], 2) for hyp, ref in zip(hypotheses, references)\n","  ]\n","  rouge_2_f, rouge_2_p, rouge_2_r = map(np.mean, zip(*rouge_2))\n","\n","  # Calculate ROUGE-L F1, precision, recall scores\n","  rouge_l = [\n","      rouge_l_sentence_level([hyp], [ref])\n","      for hyp, ref in zip(hypotheses, references)\n","  ]\n","  rouge_l_f, rouge_l_p, rouge_l_r = map(np.mean, zip(*rouge_l))\n","\n","  return {\n","      \"rouge_1/f_score\": rouge_1_f,\n","      \"rouge_1/r_score\": rouge_1_r,\n","      \"rouge_1/p_score\": rouge_1_p,\n","      \"rouge_2/f_score\": rouge_2_f,\n","      \"rouge_2/r_score\": rouge_2_r,\n","      \"rouge_2/p_score\": rouge_2_p,\n","      \"rouge_l/f_score\": rouge_l_f,\n","      \"rouge_l/r_score\": rouge_l_r,\n","      \"rouge_l/p_score\": rouge_l_p,\n","  }"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rMb_Yp2SvgVM","colab_type":"code","colab":{}},"source":["from nltk.translate.bleu_score import sentence_bleu\n","\n","def reward_function(reference, summary):\n","  final_result = {}\n","  rouge_result = rouge([summary],[reference])\n","  final_result[\"rouge_1/f_score\"] = rouge_result[\"rouge_1/f_score\"]\n","  final_result[\"rouge_1/r_score\"] = rouge_result[\"rouge_1/r_score\"]\n","  final_result[\"rouge_1/p_score\"] = rouge_result[\"rouge_1/p_score\"]\n","  final_result[\"rouge_2/f_score\"] = rouge_result[\"rouge_2/f_score\"]\n","  final_result[\"rouge_2/r_score\"] = rouge_result[\"rouge_2/r_score\"]\n","  final_result[\"rouge_2/p_score\"] = rouge_result[\"rouge_2/p_score\"]\n","  final_result[\"rouge_l/f_score\"] = rouge_result[\"rouge_l/f_score\"]\n","  final_result[\"rouge_l/r_score\"] = rouge_result[\"rouge_l/r_score\"]\n","  final_result[\"rouge_l/p_score\"] = rouge_result[\"rouge_l/p_score\"]\n","  \n","  final_result[\"bleu\"] =  sentence_bleu([reference.split()],summary.split(),weights=(0.25,0.25,0.25,0.25))\n","  \n","  return final_result"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ccqiPPSF5GC9","colab_type":"code","colab":{}},"source":["#https://pymotw.com/2/xml/etree/ElementTree/create.html\n","\n","bleu_arr = []\n","rouge_1_arr_f  = []\n","rouge_1_arr_r  = []\n","rouge_1_arr_p  = []\n","rouge_2_arr_f  = []\n","rouge_2_arr_r  = []\n","rouge_2_arr_p  = []\n","rouge_L_arr_f  = []\n","rouge_L_arr_r  = []\n","rouge_L_arr_p  = []\n","\n","from xml.etree import ElementTree\n","from xml.dom import minidom\n","from functools import reduce\n","\n","def prettify(elem):\n","    \"\"\"Return a pretty-printed XML string for the Element.\n","    \"\"\"\n","    rough_string = ElementTree.tostring(elem, 'utf-8')\n","    reparsed = minidom.parseString(rough_string)\n","    return reparsed.toprettyxml(indent=\"  \")\n","  \n","from xml.etree.ElementTree import Element, SubElement, Comment\n","\n","top = Element('ZakSum')\n","\n","def zaksum(article,reference,summary_array):\n","  comment = Comment('Generated by Amr Zaki')\n","  top.append(comment)\n","\n","  i=0\n","  for summ in summary_array:\n","    example = SubElement(top, 'example')\n","    article_element   = SubElement(example, 'article')\n","    article_element.text = article[i]\n","\n","    reference_element = SubElement(example, 'reference')\n","    reference_element.text = reference[i]\n","\n","    summary_element   = SubElement(example, 'summary')\n","    summary_element.text = summ\n","\n","    final_result = reward_function(reference[i],summ )\n","\n","    \n","    eval_element = SubElement(example, 'eval')\n","    bleu_score_element = SubElement(eval_element,'BLEU'           , {'score':str(final_result[\"bleu\"]           )})\n","    ROUGE_1_element  = SubElement(eval_element, 'ROUGE_1_f_score' , {'score':str(final_result[\"rouge_1/f_score\"])})\n","    ROUGE_1_element  = SubElement(eval_element, 'ROUGE_1_r_score' , {'score':str(final_result[\"rouge_1/r_score\"])})\n","    ROUGE_1_element  = SubElement(eval_element, 'ROUGE_1_p_score' , {'score':str(final_result[\"rouge_1/p_score\"])})\n","    ROUGE_2_element  = SubElement(eval_element, 'ROUGE_2_f_score' , {'score':str(final_result[\"rouge_2/f_score\"])})\n","    ROUGE_2_element  = SubElement(eval_element, 'ROUGE_2_r_score' , {'score':str(final_result[\"rouge_2/r_score\"])})\n","    ROUGE_2_element  = SubElement(eval_element, 'ROUGE_2_p_score' , {'score':str(final_result[\"rouge_2/p_score\"])})\n","    ROUGE_L_element  = SubElement(eval_element, 'ROUGE_l_f_score' , {'score':str(final_result[\"rouge_l/f_score\"])})\n","    ROUGE_L_element  = SubElement(eval_element, 'ROUGE_l_r_score' , {'score':str(final_result[\"rouge_l/r_score\"])})\n","    ROUGE_L_element  = SubElement(eval_element, 'ROUGE_l_p_score' , {'score':str(final_result[\"rouge_l/p_score\"])})\n","\n","    \n","    bleu_arr.append(     final_result[\"bleu\"]           ) \n","    rouge_1_arr_f.append(final_result[\"rouge_1/f_score\"]) \n","    rouge_1_arr_r.append(final_result[\"rouge_1/r_score\"]) \n","    rouge_1_arr_p.append(final_result[\"rouge_1/p_score\"]) \n","    rouge_2_arr_f.append(final_result[\"rouge_2/f_score\"]) \n","    rouge_2_arr_r.append(final_result[\"rouge_2/r_score\"]) \n","    rouge_2_arr_p.append(final_result[\"rouge_2/p_score\"]) \n","    rouge_L_arr_f.append(final_result[\"rouge_l/f_score\"]) \n","    rouge_L_arr_r.append(final_result[\"rouge_l/r_score\"]) \n","    rouge_L_arr_p.append(final_result[\"rouge_l/p_score\"]) \n","    i+=1\n","\n","  top.set('bleu', str(reduce(lambda x, y: x + y,  bleu_arr) / len(bleu_arr)))\n","  top.set('rouge_1_f_score', str(reduce(lambda x, y: x + y,  rouge_1_arr_f) / len(rouge_1_arr_f)))\n","  top.set('rouge_1_r_score', str(reduce(lambda x, y: x + y,  rouge_1_arr_r) / len(rouge_1_arr_r)))\n","  top.set('rouge_1_p_score', str(reduce(lambda x, y: x + y,  rouge_1_arr_p) / len(rouge_1_arr_p)))\n","  \n","  top.set('rouge_2_f_score', str(reduce(lambda x, y: x + y,  rouge_2_arr_f) / len(rouge_2_arr_f)))\n","  top.set('rouge_2_r_score', str(reduce(lambda x, y: x + y,  rouge_2_arr_r) / len(rouge_2_arr_r)))\n","  top.set('rouge_2_p_score', str(reduce(lambda x, y: x + y,  rouge_2_arr_p) / len(rouge_2_arr_p)))\n","  \n","  top.set('rouge_L_f_score', str(reduce(lambda x, y: x + y,  rouge_L_arr_f) / len(rouge_L_arr_f)))\n","  top.set('rouge_L_r_score', str(reduce(lambda x, y: x + y,  rouge_L_arr_r) / len(rouge_L_arr_r)))\n","  top.set('rouge_L_p_score', str(reduce(lambda x, y: x + y,  rouge_L_arr_p) / len(rouge_L_arr_p)))\n","  \n","\n","  with open(result_file, \"w\") as f:\n","    print(prettify(top), file=f)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_QInQj4Av7Gk","colab_type":"code","colab":{}},"source":["#reference_str = \"\"\" بث مباشر بدون تقطيع لمباراه مانشستر يونايتد وستوك سيتي في البريميرليج \"\"\"\n","#\n","#summary = \"\"\"بث مباشر تي في مانشستر يونايتد\"\"\"\n","#\n","#aaaa = reward_function(reference_str,summary)\n","#aaaa"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"igPicQYo60Dh","colab_type":"text"},"source":["# RUN"]},{"cell_type":"code","metadata":{"id":"-uK19rVg62QD","colab_type":"code","outputId":"e3395750-fb3d-4b67-f6d3-b75ab00d85c4","executionInfo":{"status":"ok","timestamp":1577120318858,"user_tz":-120,"elapsed":1607,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["zaksum(article,reference,summary)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/nltk/translate/bleu_score.py:490: UserWarning: \n","Corpus/Sentence contains 0 counts of 4-gram overlaps.\n","BLEU scores might be undesirable; use SmoothingFunction().\n","  warnings.warn(_msg)\n","/usr/local/lib/python3.6/dist-packages/nltk/translate/bleu_score.py:490: UserWarning: \n","Corpus/Sentence contains 0 counts of 2-gram overlaps.\n","BLEU scores might be undesirable; use SmoothingFunction().\n","  warnings.warn(_msg)\n","/usr/local/lib/python3.6/dist-packages/nltk/translate/bleu_score.py:490: UserWarning: \n","Corpus/Sentence contains 0 counts of 3-gram overlaps.\n","BLEU scores might be undesirable; use SmoothingFunction().\n","  warnings.warn(_msg)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"zvkzs3grGjny","colab_type":"code","colab":{}},"source":["import pickle\n","with open( \"drive/My Drive/Colab Notebooks/Model 2/\" + \"glove/model_glove_300.pkl\", 'rb') as handle:\n","    word_vectors = pickle.load(handle)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4AhK7RoEGrSn","colab_type":"code","outputId":"da2d60a1-2e6d-48a8-b737-8052fca4d9ce","executionInfo":{"status":"ok","timestamp":1563134973238,"user_tz":-120,"elapsed":653,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["type(word_vectors)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["gensim.models.keyedvectors.Word2VecKeyedVectors"]},"metadata":{"tags":[]},"execution_count":178}]},{"cell_type":"code","metadata":{"id":"NH3p_swhGtJm","colab_type":"code","outputId":"8b4991bb-13aa-4c6b-90cb-05aa6fc29d2f","executionInfo":{"status":"ok","timestamp":1563135043468,"user_tz":-120,"elapsed":757,"user":{"displayName":"amr zaki","photoUrl":"","userId":"09456039094530776333"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["word_vectors.vocab.keys"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'the': <gensim.models.keyedvectors.Vocab at 0x7f44e75048d0>,\n"," ',': <gensim.models.keyedvectors.Vocab at 0x7f44e74fa3c8>,\n"," '.': <gensim.models.keyedvectors.Vocab at 0x7f44e5e416a0>,\n"," 'of': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c6d8>,\n"," 'to': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c748>,\n"," 'and': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c7b8>,\n"," 'in': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c828>,\n"," 'a': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c860>,\n"," '\"': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c898>,\n"," \"'s\": <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c908>,\n"," 'for': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c978>,\n"," '-': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2c9b0>,\n"," 'that': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ca20>,\n"," 'on': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ca90>,\n"," 'is': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cb00>,\n"," 'was': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cb70>,\n"," 'said': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cbe0>,\n"," 'with': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cc50>,\n"," 'he': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ccc0>,\n"," 'as': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cd30>,\n"," 'it': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cda0>,\n"," 'by': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ce10>,\n"," 'at': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ce80>,\n"," '(': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2ceb8>,\n"," ')': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cef0>,\n"," 'from': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cf60>,\n"," 'his': <gensim.models.keyedvectors.Vocab at 0x7f44e5e2cfd0>,\n"," \"''\": <gensim.models.keyedvectors.Vocab at 0x7f44e5db6080>,\n"," '``': <gensim.models.keyedvectors.Vocab at 0x7f44e5db60f0>,\n"," 'an': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6160>,\n"," 'be': <gensim.models.keyedvectors.Vocab at 0x7f44e5db61d0>,\n"," 'has': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6240>,\n"," 'are': <gensim.models.keyedvectors.Vocab at 0x7f44e5db62b0>,\n"," 'have': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6320>,\n"," 'but': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6390>,\n"," 'were': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6400>,\n"," 'not': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6470>,\n"," 'this': <gensim.models.keyedvectors.Vocab at 0x7f44e5db64e0>,\n"," 'who': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6550>,\n"," 'they': <gensim.models.keyedvectors.Vocab at 0x7f44e5db65c0>,\n"," 'had': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6630>,\n"," 'i': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6668>,\n"," 'which': <gensim.models.keyedvectors.Vocab at 0x7f44e5db66d8>,\n"," 'will': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6748>,\n"," 'their': <gensim.models.keyedvectors.Vocab at 0x7f44e5db67b8>,\n"," ':': <gensim.models.keyedvectors.Vocab at 0x7f44e5db67f0>,\n"," 'or': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6860>,\n"," 'its': <gensim.models.keyedvectors.Vocab at 0x7f44e5db68d0>,\n"," 'one': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6940>,\n"," 'after': <gensim.models.keyedvectors.Vocab at 0x7f44e5db69b0>,\n"," 'new': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6a20>,\n"," 'been': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6a90>,\n"," 'also': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6b00>,\n"," 'we': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6b70>,\n"," 'would': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6be0>,\n"," 'two': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6c50>,\n"," 'more': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6cc0>,\n"," \"'\": <gensim.models.keyedvectors.Vocab at 0x7f44e5db6cf8>,\n"," 'first': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6d68>,\n"," 'about': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6dd8>,\n"," 'up': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6e48>,\n"," 'when': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6eb8>,\n"," 'year': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6f28>,\n"," 'there': <gensim.models.keyedvectors.Vocab at 0x7f44e5db6f98>,\n"," 'all': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc048>,\n"," '--': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc0b8>,\n"," 'out': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc128>,\n"," 'she': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc198>,\n"," 'other': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc208>,\n"," 'people': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc278>,\n"," \"n't\": <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc2e8>,\n"," 'her': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc358>,\n"," 'percent': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc3c8>,\n"," 'than': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc438>,\n"," 'over': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc4a8>,\n"," 'into': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc518>,\n"," 'last': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc588>,\n"," 'some': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc5f8>,\n"," 'government': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc630>,\n"," 'time': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc6a0>,\n"," '$': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc6d8>,\n"," 'you': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc748>,\n"," 'years': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc7b8>,\n"," 'if': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc828>,\n"," 'no': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc898>,\n"," 'world': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc908>,\n"," 'can': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc978>,\n"," 'three': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbc9e8>,\n"," 'do': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbca58>,\n"," ';': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbca90>,\n"," 'president': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcac8>,\n"," 'only': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcb38>,\n"," 'state': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcba8>,\n"," 'million': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcc18>,\n"," 'could': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcc88>,\n"," 'us': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbccf8>,\n"," 'most': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcd68>,\n"," '_': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcda0>,\n"," 'against': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbce10>,\n"," 'u.s.': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbce80>,\n"," 'so': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcef0>,\n"," 'them': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcf60>,\n"," 'what': <gensim.models.keyedvectors.Vocab at 0x7f44e5dbcfd0>,\n"," 'him': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1080>,\n"," 'united': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc10f0>,\n"," 'during': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1160>,\n"," 'before': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc11d0>,\n"," 'may': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1240>,\n"," 'since': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc12b0>,\n"," 'many': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1320>,\n"," 'while': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1390>,\n"," 'where': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1400>,\n"," 'states': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1470>,\n"," 'because': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc14e0>,\n"," 'now': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1550>,\n"," 'city': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc15c0>,\n"," 'made': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1630>,\n"," 'like': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc16a0>,\n"," 'between': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1710>,\n"," 'did': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1780>,\n"," 'just': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc17f0>,\n"," 'national': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1828>,\n"," 'day': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1898>,\n"," 'country': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1908>,\n"," 'under': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1978>,\n"," 'such': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc19e8>,\n"," 'second': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1a58>,\n"," 'then': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1ac8>,\n"," 'company': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1b38>,\n"," 'group': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1ba8>,\n"," 'any': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1c18>,\n"," 'through': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1c88>,\n"," 'china': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1cf8>,\n"," 'four': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1d68>,\n"," 'being': <gensim.models.keyedvectors.Vocab at 0x7f44e5dc1dd8>,\n"," 'down': <gensim.models.keyedvectors.Vocab at 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